- Part 1 Recap
- Part 2 Goals
- Jupyter (IPython) Notebook
part 1 recap
In part 1 of this series we got a feel for Markov Models, Hidden Markov Models, and their applications. We went through the process of using a hidden Markov model to solve a toy problem involving a pet dog. We concluded the article by going through a high level quant finance application of Gaussian mixture models to detect historical regimes.
part 2 goals
In this post, my goal is to impart a basic understanding of the expectation maximization algorithm which, not only forms the basis of several machine learning algorithms, including K-Means, and Gaussian mixture models, but also has lots of applications beyond finance. We will also cover the K-Means algorithm which is a form of EM, and its weaknesses. Finally we will discuss how Gaussian mixture models improve on several of K-Means weaknesses.
This post is structured as a Jupyter (IPython) Notebook. I used several different resources\references and tried to give proper credit. Please contact me if you find errors, have suggestions, or if any sources were not attributed correctly.
- Python Data Science Handbook by Jake Vanderplas
- Python Machine Learning by Sebastian Raschka
I receive a small commission from Amazon if any of the above books are purchased using a link from my website.